Infocare Healthcare enhances clinical data insights with AI-powered analytics on public cloud
This global healthcare IT provider is modernizing its infrastructure, automating query generation and improving clinician decision-making with Amazon Q.
Industry
Healthcare
Challenge
Modernize healthcare data analytics using AI while addressing compliance, security and scalability needs. Reduce reliance on manual SQL queries and improve data accessibility for clinicians and analysts.
Outcome By integrating Amazon Q’s AI-powered analytics, Infocare enhances its ability to process and extract value from healthcare data, streamlining insights for clinicians and analysts. Solutions Amazon Q Business, Cloud Optimization, Data, Artificial Intelligence & Machine Learning, Security, Compliance & Governance, AI, Foundry for AI by Rackspace (FAIR™) Platforms Amazon Web Services (AWS)Customer
Infocare Healthcare is a global IT services provider specializing in managed IT solutions, cybersecurity, digital transformation and healthcare data analytics. Established in 2004, Infocare operates internationally with offices in the United States, Ireland and the United Kingdom. This global presence enables Infocare to deliver secure, scalable and efficient IT services that empower businesses to navigate digital transformation while meeting regulatory and security requirements.
Central to Infocare's healthcare offerings is their flagship product, Soteria®, a clinical documentation tool designed by clinicians for clinicians. Soteria enhances patient care and operational efficiency by automating tasks, reducing workloads and offering providers more time to focus on patients. It supports patient visit activities throughout the continuum of care, enabling a comprehensive 360° view of the patient, which accelerates care coordination and engages all factors integral to the patient experience.
Over the past decade, Infocare has also developed a comprehensive data registry designed to manage illnesses across extensive patient populations. This registry serves as a valuable tool for healthcare providers, offering insights into disease patterns and treatment outcomes while enhancing patient care and supporting clinicians in making informed decisions.
“Having a robust registry data set with helpful analytics will provide aid and assistance for better diagnosis or treatment of the illness. This is a scenario where the technology can support the health outcomes of patients.”
Situation
Infocare Healthcare was already engaged with Rackspace Technology® through Rackspace Optimizer+ for AWS platform support when the announcement of Amazon Q introduced a new opportunity. Infocare’s leadership saw the potential of AI to enhance its healthcare registry system, which tracks patient conditions and disease patterns across medical centers.
This registry, built on AWS in August 2023, serves as a critical tool for healthcare providers by offering insights into disease trends, treatment outcomes and potential areas for clinical focus. “Having a robust registry data set with helpful analytics will provide aid and assistance for better diagnosis or treatment of the illness,” said Fiacre McCullough, Solution Architect at Infocare Healthcare. “This is a scenario where the technology can support the health outcomes of patients.”
Driven by curiosity and a commitment to continuous improvement, Infocare wanted to explore how AI-powered analytics could enhance access to registry insights. It hoped that through the power of machine learning and natural language processing, clinicians and analysts could make more informed decisions about patient care and disease management.
Specifically, the company wanted to determine whether Amazon Q could streamline data retrieval by replacing manual SQL queries, which were time-consuming and created bottlenecks in the analysis process. “We were already in the AWS ecosystem, so it was easier for us to dip our toes into using Amazon Q,” McCullough said.
In addition, Infocare wanted to test Amazon QuickSight as a way to visually represent trends within the registry data, allowing clinicians and analysts to more easily interpret patterns in patient conditions and disease progression. This proof of concept aligned with Infocare’s broader mission of supporting healthcare professionals with meaningful, data-driven insights that could improve diagnoses, enhance treatments and identify trends that might otherwise go unnoticed.
“The end goal is to have a robust data set with good analytics to aid in better diagnosis and treatment of illnesses,” said McCullough. “We're also working to identify trends that could help analysts and doctors recognize areas of focus they might not have foreseen before.”
As a company that thrives on exploration and innovation, Infocare approached the AI integration as an opportunity to experiment with new technologies. The team was validating the feasibility of AI-driven insights for registry data and assessing how emerging solutions could complement its existing security, compliance and operational frameworks. With help from the Foundry for AI by Rackspace (FAIR™), Infocare wanted to integrate Amazon Q as part of a proof of concept, evaluating its potential to enhance healthcare analytics while maintaining the high standards of accuracy and reliability required in the industry.
"AI is a powerful tool, but we have to be careful. We'll take all the time we need to trial use cases, ensuring the data is accurately represented and that clinicians can trust the insights."
Solution
Infocare worked with FAIR™ to explore how Amazon Q and Amazon QuickSight could enhance the accessibility and usability of its healthcare registry data. The goal was to reduce reliance on manual SQL queries and allow clinicians and analysts to retrieve insights faster and more intuitively.
Amazon Q provided out-of-the-box AI chatbot functionality, allowing Infocare to quickly test how natural language processing (NLP) could improve registry data retrieval. Instead of writing SQL queries manually, users could simply ask questions and receive AI-generated responses, making the system more user-friendly and efficient.
In parallel, Infocare tested Amazon QuickSight as a way to visualize data trends and patterns within the registry system. QuickSight’s AI-driven analytics enabled the team to experiment with dynamic dashboards and automated reporting, with the goal of helping healthcare professionals analyze disease progression, treatment effectiveness and potential focus areas. “With QuickSight, you're getting visual responses back as well, which is really nice to see,” McCullough said.
Throughout the project, Rackspace and FAIR™ collaborated closely with Infocare, providing AI expertise, technical guidance and hands-on support in configuring metadata within QuickSight Topics to enhance query accuracy. This partnership helped to align Infocare’s AI-driven analytics goals with its broader mission of delivering meaningful, data-driven insights to clinicians and analysts.
By integrating AI-powered analytics into its registry, Infocare showcased how automation can streamline data retrieval and analysis, enabling clinicians and analysts to uncover patient conditions and disease trends with greater speed and accuracy.
AI-driven analytics with Amazon Q and QuickSight accelerated clinician decision-making by eliminating manual SQL queries for registry data analysis.
Outcome
Infocare Healthcare’s proof of concept (PoC) with Amazon Q and Amazon QuickSight demonstrated how AI-driven analytics could accelerate data retrieval and enhance decision-making in healthcare. By integrating these technologies, Infocare explored whether AI could reduce reliance on manual SQL queries while enabling clinicians and analysts to retrieve critical insights more efficiently.
Amazon Q’s out-of-the-box AI chatbot functionality allowed Infocare to begin testing much sooner than if it had to build a custom LLM from scratch.
“We were already hosted on AWS, so when Rackspace suggested Amazon Q for our AI project, we were open to trying it,” McCullough said. “If we had needed to create our own LLM, we would have done it, but it would have delayed the prioritization of this project. Amazon Q gave us a way to start experimenting earlier than we otherwise could have.”
The PoC also demonstrated the potential of Amazon QuickSight for visualizing trends within registry data. Through the use of AI-driven insights, clinicians and analysts will be able to more intuitively identify patterns in disease progression in support of improved diagnoses, treatment plans and long-term patient care strategies.
Key benefits of the PoC:
- Faster insights: AI-powered analytics significantly reduced the time required to generate data-driven insights, improving efficiency for clinicians and analysts.
- Enhanced usability: Amazon Q’s natural language capabilities allowed users to interact with registry data more intuitively, reducing reliance on complex queries.
- Data visualization: QuickSight enabled dynamic reporting and trend analysis, helping healthcare professionals make informed decisions.
While the AI implementation is still in testing, Infocare is actively refining the solution based on early user feedback, enhancing data visualization and improving reporting capabilities. "AI is a powerful tool, but we have to be careful,” said McCullough. “We'll take all the time we need to trial use cases, ensuring the data is accurately represented and that clinicians can trust the insights."
Beyond the technical benefits, the collaboration with Rackspace Technology was instrumental in Infocare’s AI adoption. The FAIR™ team provided guidance on AI integration, assisted with QuickSight’s AI-driven capabilities and helped refine metadata configurations for more accurate queries.
“Rackspace provided valuable guidance in structuring our AI integration,” McCullough noted. “Their team felt like an extension of ours, helping us navigate QuickSight’s capabilities and refine our data models.”
Through its successful demonstration of the feasibility of AI-powered analytics, Infocare is now positioned to make data-driven improvements to its healthcare registry system, ultimately supporting clinicians with more efficient, intelligent insights into patient conditions and disease patterns. “The next phase of the project is to continue refining AI-generated queries, improving accuracy and determining how clinicians can effectively use this system in a live healthcare environment,” McCullough said.
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